Architecting MLOps: The Journey from Identifying ML Use Cases to the ML Platform Architecture


Studio B

One of the best practices we know from great engineers is the back-of-the-envelope calculation to estimate costs and resources. In Machine Learning Engineering, we all would benefit from such a “back-of-the-envelope calculation” skill to design an ML system. We need to confirm - as cheaply as possible - that our future ML project is worthwhile and will solve a business problem, and that the costs and resources will be feasible. In this talk, I present a collaborative design toolkit for ML projects that supports identifying ML use cases and performing rough prototyping by using three canvases: Machine Learning Canvas, Data Landscape Canvas, and MLOps Stack Canvas.


Dr. Larysa Visengeriyeva

Head of Data and AI. creator

Dr. Larysa Visengeriyeva received her doctorate in Augmented Data Quality Management at TU Berlin. She is a technology consultant supporting INNOQ customers with their technology transformation. She focuses on Machine Learning Operations (MLOps), Data Architectures like Data Mesh, and Domain-Driven Design. Larysa initiated the Women+ in Data and AI Summer Festival 2023 and organized the Ukrainian chapter of Women in Machine Learning and Data Science.

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